This letter discusses previously published research that paves the way for deeper exploration of the ethical and human aspects of artificial intelligence in health care.
OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitali...
OBJECTIVE: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patie...
OBJECTIVES: Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs),...
OBJECTIVES: Current models for patient risk prediction rely on practitioner expertise and domain knowledge. This study presents a deep learning model-a type of machine learning that does not require human inputs-to analyze complex clinical and financ...
This commentary explores the current state, challenges, and potential of artificial intelligence (AI) in health care revenue cycle management, emphasizing collaboration, data standardization, and targeted implementation to enhance adoption.
OBJECTIVES: To implement a technology-based, systemwide readmission reduction initiative in a safety-net health system and evaluate clinical, care equity, and financial outcomes.
OBJECTIVES: We assessed whether proactive care management for artificial intelligence (AI)-identified at-risk patients reduced preventable emergency department (ED) visits and hospital admissions (HAs).
OBJECTIVES: In 2018, CMS established reimbursement for the first Medicare-covered artificial intelligence (AI)-enabled clinical software: CT fractional flow reserve (FFRCT) to assist in the diagnosis of coronary artery disease. This study quantified ...
OBJECTIVES: To understand whether and how equity is considered in artificial intelligence/machine learning governance processes at academic medical centers.